How do I reduce hallucinations with GLM-5 in production?

You reduce hallucinations with GLM-5 in production by changing the system design, not by hoping the model “tries harder.” The most effective pattern is RAG + strict grounding + validation: retrieve authoritative context, instruct GLM-5 to answer only from that context, and reject outputs that violate your rules. GLM-5’s own positioning emphasizes long-horizon agentic work and tool use (see the official overview and migration docs), which pairs naturally with “don’t guess—call a tool.” In practice, that means if the answer isn’t in the retrieved context, GLM-5 should either (a) ask a clarifying question, or (b) explicitly say it can’t find the answer in the provided sources. Start from the official GLM-5 docs for model behavior and tool calling: GLM-5 overview, Function Calling, and Migrate to GLM-5. The GLM-5 launch post also calls out attention to hallucination reduction and agent workflows: GLM-5 blog.

A reliable production recipe looks like this “three-layer guardrail” checklist:

Layer 1 — Retrieval grounding (must-have)

• Store your docs in a vector database such as Milvus or Zilliz Cloud (managed Milvus).

• Retrieve top-k chunks with metadata filters (product, version, lang, doc_type).

• Inject chunks into a ## Context section, each with an ID and URL.

• System rule: “Use only Context. If missing, say ‘Not in provided context.’”

Layer 2 — Output contract + validator (high leverage)

• Require a fixed output schema (Markdown sections, or JSON).

• Validate the output in code (JSON schema, required sections, max length).

• If invalid, re-prompt with the validation error (“Your response missed Sources.”).

Layer 3 — Tool-first behavior (prevents guessing)

• Provide tools like search_docs, get_doc_by_url, lookup_version.

• Allow GLM-5 to call tools instead of inventing facts (Z.ai documents tool calling and streaming tool args): Function Calling and Streaming / tool streaming.

If you want a concrete “anti-hallucination prompt,” this works well:

• System: “Answer only from Context. If unsure, ask one clarification. Never invent APIs.”

• Output: “Include ### Answer and ### Sources listing chunk IDs.”

Finally, measure hallucinations instead of debating them. Log retrieval inputs (chunk IDs + similarity scores), log outputs, and sample failures weekly. When a wrong answer happens, you can usually categorize it quickly: retrieval failure (wrong chunks), prompt failure (rules too weak), or model drift (needs stronger refusal behavior). Retrieval failures are often solved by better chunking/metadata and more precise filters in Milvus / Zilliz Cloud. Prompt failures are solved by stronger system instructions and stricter validators. If you want a benchmark mindset, multi-turn hallucination is now being studied explicitly in research, which aligns with the idea that grounding must persist across turns (example paper: HalluHard benchmark). The goal isn’t “zero mistakes,” it’s predictable behavior: GLM-5 should either answer from sources or clearly say it cannot.